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Predicting biathlon shooting performance using machine learning.

Thomas Maier1, Daniel Meister2, Severin Trösch1

  • 1a Swiss Federal Institute of Sport , Section for Elite Sport , Magglingen , Switzerland.

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|March 23, 2018
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Summary
This summary is machine-generated.

Biathlon shooting performance is influenced by discipline and position, with sprint and standing shots being more challenging. While machine learning models can predict future shots, athlete hit rates remain the most significant factor.

Keywords:
Sportcompetitionmodelling

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Area of Science:

  • Sports Science
  • Biathlon Performance Analysis
  • Machine Learning in Sports

Background:

  • Biathlon shooting accuracy significantly impacts competition outcomes.
  • Predicting shooting performance is crucial for athlete strategy and training.
  • Previous research has not fully explored the predictability of biathlon shooting hits and misses.

Purpose of the Study:

  • To identify key factors influencing biathlon shooting performance.
  • To develop predictive models for future shooting success (hits and misses).

Main Methods:

  • Analysis of 118,300 shots across four competitive seasons.
  • Training and evaluation of various machine learning models (e.g., tree-based boosting, logistic regression, neural networks).
  • Prediction of 34,340 shots for the subsequent season.

Main Results:

  • Lower hit rates observed in sprint and pursuit disciplines versus individual and mass start.
  • Standing shooting positions yielded lower hit rates compared to prone.
  • The 1st prone and 5th standing shots showed reduced accuracy.
  • A tree-based boosting model achieved an AUC of 0.62 for predicting future shots.
  • An athlete's preceding mode-specific hit rate was the dominant predictive factor.

Conclusions:

  • Biathlon shooting performance is influenced by discipline, shooting position, and shot sequence.
  • Machine learning models offer moderate predictive capability for biathlon shooting.
  • Athlete's historical shooting accuracy is the strongest predictor, with inherent randomness persisting.
  • Focusing on improving overall mode-specific hit rates is recommended for athletes.